Automation and alarm systems (such as home automation systems, fire alarm systems, and security systems) typically include one or more gateway entities (e.g., alarm panels) that receive information from various sensors distributed through a structured area. In response to particular types of input signals, the sensors or the gateway entity sometimes trigger an action by an output device. For example, a typical fire alarm system includes one or more sensors (e.g., smoke detectors or manually-actuated pull stations, etc.) and output devices (e.g., strobes, sirens, public announcement systems, etc.) operably connected to a gateway entity.
In some traditional automation and alarm systems, a sensor includes processing capability for processing the sensor's data. For example, a sensor monitors electrical signals collected by the sensor's detection device for variations that represent the occurrence of an alarm condition. For purposes of logging data and performing data analytics, the sensor forwards information to the gateway entity, and the gateway entity in turn forwards the data to a cloud processing device. The cloud processing device collects data from many sensors and/or gateway entities, analyzes the data, and generates reports.
Processing the data at the sensor requires that the sensor consume power, which can be problematic (especially if the sensor is powered by batteries). Moreover, individual sensors are relatively complex and expensive, because the sensor must be provided with sufficient processing resources so that the sensor can process its own data in isolation. During idle periods, these processing resources go unused and hence are wasted. Still further, if a new update developed for the algorithm that processes the sensor data, each sensor must receive and process the update. This updating process can be complicated and time consuming in an environment with many sensors.